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Volume 16, issue 5
Atmos. Chem. Phys., 16, 3041–3059, 2016
https://doi.org/10.5194/acp-16-3041-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 16, 3041–3059, 2016
https://doi.org/10.5194/acp-16-3041-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Mar 2016

Research article | 09 Mar 2016

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 2: Assessing the influence of vapor wall losses

Christopher D. Cappa1, Shantanu H. Jathar2, Michael J. Kleeman1, Kenneth S. Docherty3, Jose L. Jimenez4, John H. Seinfeld5, and Anthony S. Wexler1 Christopher D. Cappa et al.
  • 1Department of Civil and Environmental Engineering, University of California, Davis, CA, USA
  • 2Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA
  • 3Alion Science and Technology, Research Triangle Park, NC, USA
  • 4Cooperative Institute for Research in Environmental Sciences and Department Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA
  • 5Division of Chemistry and Chemical Engineering and Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

Abstract. The influence of losses of organic vapors to chamber walls during secondary organic aerosol (SOA) formation experiments has recently been established. Here, the influence of such losses on simulated ambient SOA concentrations and properties is assessed in the University of California at Davis / California Institute of Technology (UCD/CIT) regional air quality model using the statistical oxidation model (SOM) for SOA. The SOM was fit to laboratory chamber data both with and without accounting for vapor wall losses following the approach of Zhang et al. (2014). Two vapor wall-loss scenarios are considered when fitting of SOM to chamber data to determine best-fit SOM parameters, one with “low” and one with “high” vapor wall-loss rates to approximately account for the current range of uncertainty in this process. Simulations were run using these different parameterizations (scenarios) for both the southern California/South Coast Air Basin (SoCAB) and the eastern United States (US). Accounting for vapor wall losses leads to substantial increases in the simulated SOA concentrations from volatile organic compounds (VOCs) in both domains, by factors of  ∼  2–5 for the low and  ∼  5–10 for the high scenarios. The magnitude of the increase scales approximately inversely with the absolute SOA concentration of the no loss scenario. In SoCAB, the predicted SOA fraction of total organic aerosol (OA) increases from  ∼  0.2 (no) to  ∼  0.5 (low) and to  ∼  0.7 (high), with the high vapor wall-loss simulations providing best general agreement with observations. In the eastern US, the SOA fraction is large in all cases but increases further when vapor wall losses are accounted for. The total OA ∕ ΔCO ratio captures the influence of dilution on SOA concentrations. The simulated OA ∕ ΔCO in SoCAB (specifically, at Riverside, CA) is found to increase substantially during the day only for the high vapor wall-loss scenario, which is consistent with observations and indicative of photochemical production of SOA. Simulated O : C atomic ratios for both SOA and for total OA increase when vapor wall losses are accounted for, while simulated H : C atomic ratios decrease. The agreement between simulations and observations of both the absolute values and the diurnal profile of the O : C and H : C atomic ratios for total OA was greatly improved when vapor wall-losses were accounted for. These results overall demonstrate that vapor wall losses in chambers have the potential to exert a large influence on simulated ambient SOA concentrations, and further suggest that accounting for such effects in models can explain a number of different observations and model–measurement discrepancies.

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Losses of vapors to walls of chambers can negatively bias SOA formation measurements, consequently leading to low predicted SOA concentrations in air quality models. Here, we show that accounting for such vapor losses leads to substantial increases in the predicted amount of SOA formed from VOCs and to notable increases in the O : C atomic ratio in two US regions. Comparison with a variety of observational data suggests generally improved model performance when vapor wall losses are accounted for.
Losses of vapors to walls of chambers can negatively bias SOA formation measurements,...
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